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Privacy & Security

Privacy & Security Blog Posts#

Hacking deep learning: model inversion attack by example

Hacking deep learning: model inversion attack by example

Compared to other applications, deep learning models might not seem too likely as victims of privacy attacks. However, methods exist to determine whether an entity was used in the training set (an adversarial attack called member inference), and techniques subsumed under "model inversion" allow to reconstruct raw data input given just model output (and sometimes, context information). This post shows an end-to-end example of model inversion, and explores mitigation strategies using TensorFlow Privacy

Sigrid Keydana

May 15, 2020

R Privacy & Security TensorFlow/Keras

blog post

Towards privacy: Encrypted deep learning with Syft and Keras

Towards privacy: Encrypted deep learning with Syft and Keras

Deep learning need not be irreconcilable with privacy protection. Federated learning enables on-device, distributed model training; encryption keeps model and gradient updates private; differential privacy prevents the training data from leaking. As of today, private and secure deep learning is an emerging technology. In this post, we introduce Syft, an open-source framework that integrates with PyTorch as well as TensorFlow. In an example use case, we obtain private predictions from a Keras model

Sigrid Keydana

Apr 29, 2020

R Privacy & Security TensorFlow/Keras

blog post

A first look at federated learning with TensorFlow

A first look at federated learning with TensorFlow

The term "federated learning" was coined to describe a form of distributed model training where the data remains on client devices, i.e., is never shipped to the coordinating server. In this post, we introduce central concepts and run first experiments with TensorFlow Federated, using R

Sigrid Keydana

Apr 8, 2020

Privacy & Security TensorFlow/Keras

blog post

Differential Privacy with TensorFlow

Differential Privacy guarantees that results of a database query are basically independent of the presence in the data of a single individual. Applied to machine learning, we expect that no single training example influences the parameters of the trained model in a substantial way. This post introduces TensorFlow Privacy, a library built on top of TensorFlow, that can be used to train differentially private deep learning models from R

Sigrid Keydana

Dec 20, 2019

Privacy & Security TensorFlow/Keras Time Series